Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research

Mental health services are experiencing notable transformations as innovative technologies and artificial intelligence (AI) are increasingly utilized in a growing number of studies and services.These cutting-edge technologies carry the promise of substantial improvements in the field of mental health. Nevertheless, questions emerge about the alignment of novel technologies and AI systems with human needs, especially in the context of vulnerable populations receiving mental healthcare. The practice-oriented research (POR) model is pivotal in seamlessly integrating these emerging technologies into clinical research and practice. It underscores the importance of tight collaboration between clinicians and researchers, all driven by the central goal of ensuring and elevating client well-being. This paper focuses on how novel technologies can enhance the POR model and highlights its pivotal role in integrating these technologies into clinical research and practice. We discuss two key phases: pre-treatment, and during treatment. For each phase, we describe the challenges, present the major technological innovations, describe recent studies exemplifying technology use, and suggest future directions. Ethical concerns and the importance of aligning humans and technology are also considered, in addition to implications for practice and training.

Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention

BACKGROUND

Lifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss.

METHODS

SureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m2), with the aim of losing weight over 24 weeks in a multinational field trial. SureMediks consists of a mobile app, an Internet-connected scale, and a discipline of artificial intelligence called Expert system to provide individualized guidance and weight-loss management.

RESULTS

All participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program.

CONCLUSION

An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.

ChatGPT for generating multiple-choice questions: Evidence on the use of artificial intelligence in automatic item generation for a rational pharmacotherapy exam

PURPOSE

Artificial intelligence, specifically large language models such as ChatGPT, offers valuable potential benefits in question (item) writing. This study aimed to determine the feasibility of generating case-based multiple-choice questions using ChatGPT in terms of item difficulty and discrimination levels.

METHODS

This study involved 99 fourth-year medical students who participated in a rational pharmacotherapy clerkship carried out based-on the WHO 6-Step Model. In response to a prompt that we provided, ChatGPT generated ten case-based multiple-choice questions on hypertension. Following an expert panel, two of these multiple-choice questions were incorporated into a medical school exam without making any changes in the questions. Based on the administration of the test, we evaluated their psychometric properties, including item difficulty, item discrimination (point-biserial correlation), and functionality of the options.

RESULTS

Both questions exhibited acceptable levels of point-biserial correlation, which is higher than the threshold of 0.30 (0.41 and 0.39). However, one question had three non-functional options (options chosen by fewer than 5% of the exam participants) while the other question had none.

CONCLUSIONS

The findings showed that the questions can effectively differentiate between students who perform at high and low levels, which also point out the potential of ChatGPT as an artificial intelligence tool in test development. Future studies may use the prompt to generate items in order for enhancing the external validity of the results by gathering data from diverse institutions and settings.

An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review

PURPOSE

Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools.

METHODS

Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar.

RESULTS

Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability.

CONCLUSIONS

Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial.

LEVEL OF EVIDENCE: 4

Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation

INTRODUCTION

Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation.

METHODS

This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists.

RESULTS

We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation.

CONCLUSIONS

This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas.

Evaluating false-positive detection in a computer-aided detection system for colonoscopy

BACKGROUND AND AIM

Computer-aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false-positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP.

METHODS

We analyzed CADe-assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre- and post-update using 1:1 propensity score matching.

RESULTS

During the study period, 191 colonoscopy videos (94 and 97 in the pre- and post-update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post-update group than those in the pre-update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post-update (pre-update vs post-update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post-update vs pre-update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post-update vs pre-update: 52.1% vs 49.3%; P = 0.87).

CONCLUSIONS

The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists.

Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI

BACKGROUND

To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images.

METHODS

Retrospective, ethically approved study. DDPM- and GAN-reconstructed single-slice subtraction images of 50 breasts with enhancing lesions were compared to original ones at three dose levels (25%, 10%, 5%) using quantitative measures and radiologic evaluations. Two radiologists stated their preference based on the reconstruction quality and scored the lesion conspicuity as compared to the original, blinded to the model. Fifty lesion-free maximum intensity projections were evaluated for the presence of false-positives. Results were compared between models and dose levels, using generalized linear mixed models.

RESULTS

At 5% dose, both radiologists preferred the GAN-generated images, whereas at 25% dose, both radiologists preferred the DDPM-generated images. Median lesion conspicuity scores did not differ between GAN and DDPM at 25% dose (5 versus 5, p = 1.000) and 10% dose (4 versus 4, p = 1.000). At 5% dose, both readers assigned higher conspicuity to the GAN than to the DDPM (3 versus 2, p = 0.007). In the lesion-free examinations, DDPM and GAN showed no differences in the false-positive rate at 5% (15% versus 22%), 10% (10% versus 6%), and 25% (6% versus 4%) (p = 1.000).

CONCLUSIONS

Both GAN and DDPM yielded promising results in low-dose image reconstruction. However, neither of them showed superior results over the other model for all dose levels and evaluation metrics. Further development is needed to counteract false-positives.

RELEVANCE STATEMENT

For MRI-based breast cancer screening, reducing the contrast agent dose is desirable. Diffusion probabilistic models and generative adversarial networks were capable of retrospectively enhancing the signal of low-dose images. Hence, they may supplement imaging with reduced doses in the future.

KEY POINTS

• Deep learning may help recover signal in low-dose contrast-enhanced breast MRI. • Two models (DDPM and GAN) were trained at different dose levels. • Radiologists preferred DDPM at 25%, and GAN images at 5% dose. • Lesion conspicuity between DDPM and GAN was similar, except at 5% dose. • GAN and DDPM yield promising results in low-dose image reconstruction.

Evaluation of the accuracy of ChatGPT's responses to and references for clinical questions in physical therapy

[Purpose] This study evaluated the accuracy of ChatGPT's responses to and references for five clinical questions in physical therapy based on the Physical Therapy Guidelines and assessed this language model's potential as a tool for supporting clinical decision-making in the rehabilitation field. [Participants and Methods] Five clinical questions from the "Stroke", "Musculoskeletal disorders", and "Internal disorders" sections of the Physical Therapy Guidelines, released by the Japanese Society of Physical Therapy, were presented to ChatGPT. ChatGPT was instructed to provide responses in Japanese accompanied by references such as PubMed IDs or digital object identifiers. The accuracy of the generated content and references was evaluated by two assessors with expertise in their respective sections by using a 4-point scale, and comments were provided for point deductions. The inter-rater agreement was evaluated using weighted kappa coefficients. [Results] ChatGPT demonstrated adequate accuracy in generating content for clinical questions in physical therapy. However, the accuracy of the references was poor, with a significant number of references being non-existent or misinterpreted. [Conclusion] ChatGPT has limitations in reference selection and reliability. While ChatGPT can offer accurate responses to clinical questions in physical therapy, it should be used with caution because it is not a completely reliable model.

Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm

BACKGROUND

In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE.

METHODS

A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively.

RESULTS

The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001).

CONCLUSIONS

CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.

Practice-Oriented Research: An Introduction to New Developments and Future Directions

Aimed at understanding and improving psychological therapies as they are conducted in clinical routine, practice-oriented research (POR) is now a well-established approach to the scientific foundations of mental health care services. Resting on the accumulation of a wide range of practice-based evidence related to treatment outcome and process, as well as factors associated with the participants of psychotherapy and its context, POR is ripe for new developments - regarding what to investigate and how to investigate it. This paper is the introduction of a series devoted to recent advances and future directions of POR as their pertained to routine outcome monitoring, technologies and artificial intelligence, the integration of constructs and methods from program evaluation and implementation science, and the investigation of populations with limited financial resources across various regions of the world. The series also includes commentaries from two leaders of POR.

Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study

In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.

Strengths and weaknesses of current and future prospects of artificial intelligence-mounted technologies applied in the development of pharmaceutical products and services

Starting from drug discovery, through research and development, to clinical trials and FDA approval, artificial intelligence (AI) plays a vital role in planning, developing, assessing modelling, and optimization of product attributes. In recent decades, machine-learning algorithms integrated into artificial neural networks, neuro-fuzzy logic and decision trees have been applied to tremendous domains related to drug formulation development. Optimized formulations were transformed from lab to market based on optimized properties derived from AI Technologies. Research and development in pharmaceutical industry rely upon computer-driven equipment and machine learning technology to extract data, perform simulations, modelling, and optimization to get optimum solutions. Merging AI technologies in various steps of pharmaceutical manufacture is a major challenge due to lack of in-house technologies. In silico studies based on artificial intelligence are widely applied as effective tools to screen the market needs of medications and pharmaceutical services through inspecting scientific literature and prioritizing medicines for specific illnesses or a particular patient. Specialized personnel who excel in scientific and data science with analytical knowledge are essential for transformation to smart manufacturing and offering services. However, privacy, cybersecurity, AI-dependent unemployment, and ownership rights of AI technologies require proper regulations to gain the benefits and minimize the drawbacks.

Insights into levofloxacin adsorption with machine learning models using nano-composite hydrochars

Hydrothermal carbonization was applied to taro peel wastes to produce hydrochars using a facile and environmentally friendly process. Four different entities were prepared: hydrochar (TPh), phosphoric-activated hydrochar (P-TPh), and silver@hydrochars (Ag@TPh, Ag@P-TPh). The elemental compositions of the single and composite hydrochars were confirmed by EDX. Among the produced hydrochars, the morphology of the Ag@hydrochar composites demonstrated more wrinkled structure, and Ag nanoparticles decorated the surface. The optimal experimental conditions for levofloxacin adsorption were determined to be a contact time of 45 min, hydrochar dose of 0.15 g L-1, and pH of 7. The best adsorption performances were assigned to Ag@hydrochars. Two machine learning models were applied to predict the levofloxacin adsorption efficiency of the Ag@hydrochars. A central composite design (CCD) and a 3-10-1 artificial neural network (ANN) model were developed to estimate the removal performance of levofloxacin using Levenberg-Marquardt backpropagation algorithm based on correlation and error analysis of the adopted training functions. Furthermore, the ANN sensitivity analysis revealed the order of the relative importance variable as initial concentration> hydrochar dose> pH. The predicted values of the CCD and ANN models fitted the experimental results with R2> 0.989. Therefore, the applied models were effective in predicting levofloxacin removal under different operating conditions. This work provides an open option for the sustainable management of food industry wastes and the possibility of waste valorization to effective hydrochar composites to be applied in water treatment processes.

Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage?

INTRODUCTION

Chat-GPT is rapidly emerging as a promising and potentially revolutionary tool in medicine. One of its possible applications is the stratification of patients according to the severity of clinical conditions and prognosis during the triage evaluation in the emergency department (ED).

METHODS

Using a randomly selected sample of 30 vignettes recreated from real clinical cases, we compared the concordance in risk stratification of ED patients between healthcare personnel and Chat-GPT. The concordance was assessed with Cohen's kappa, and the performance was evaluated with the area under the receiver operating characteristic curve (AUROC) curves. Among the outcomes, we considered mortality within 72 h, the need for hospitalization, and the presence of a severe or time-dependent condition.

RESULTS

The concordance in triage code assignment between triage nurses and Chat-GPT was 0.278 (unweighted Cohen's kappa; 95% confidence intervals: 0.231-0.388). For all outcomes, the ROC values were higher for the triage nurses. The most relevant difference was found in 72-h mortality, where triage nurses showed an AUROC of 0.910 (0.757-1.000) compared to only 0.669 (0.153-1.000) for Chat-GPT.

CONCLUSIONS

The current level of Chat-GPT reliability is insufficient to make it a valid substitute for the expertise of triage nurses in prioritizing ED patients. Further developments are required to enhance the safety and effectiveness of AI for risk stratification of ED patients.

An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)

BACKGROUND AND OBJECTIVE

In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption of Clinical Decision Support Systems (CDSSs) based on Artificial Intelligence (AI) algorithms. However, the black-box nature of AI algorithms has hampered a large adoption of AI-based CDSSs in clinical practice. In this study, an innovative AI-based method to compress AI-based prediction models into explainable, model-agnostic, and reduced decision support systems (NEAR) with application to healthcare is presented and validated.

METHODS

NEAR is based on the Shapley Additive Explanations framework and can be applied to complex input models to obtain the contributions of each input feature to the output. Technically, the simplified NEAR models approximate contributions from input features using a custom library and merge them to determine the final output. Finally, NEAR estimates the confidence error associated with the single input feature contributing to the final score, making the result more interpretable. Here, NEAR is evaluated on a clinical real-world use case, the mortality prediction in patients who experienced Acute Coronary Syndrome (ACS), applying three different Machine Learning/Deep Learning models as implementation examples.

RESULTS

NEAR, when applied to the ACS use case, exhibits performances like the ones of the AI-based model from which it is derived, as in the case of the Adaptive Boosting classifier, whose Area Under the Curve is not statistically different from the NEAR one, even the model's simplification. Moreover, NEAR comes with intrinsic explainability and modularity, as it can be tested on the developed web application platform (https://neardashboard.pythonanywhere.com/).

CONCLUSIONS

An explainable and reliable CDSS tailored to single-patient analysis has been developed. The proposed AI-based system has the potential to be used alongside the clinical guidelines currently employed in the medical setting making them more personalized and dynamic and assisting doctors in taking their everyday clinical decisions.

Comparing ChatGPT and Bing, in response to the Home Blood Pressure Monitoring (HBPM) knowledge checklist

High blood pressure is one of the major public health problems that is prevalent worldwide. Due to the rapid increase in the number of users of artificial intelligence tools such as ChatGPT and Bing, it is expected that patients will use these tools as a source of information to obtain information about high blood pressure. The purpose of this study is to check the accuracy, completeness, and reproducibility of answers provided by ChatGPT and Bing to the knowledge questionnaire of blood pressure control at home. In this study, ChatGPT and Bing's responses to the HBPM 10-question knowledge checklist on blood pressure measurement were independently reviewed by three cardiologists. The mean accuracy rating of ChatGPT was 5.96 (SD = 0.17) indicating the responses were highly accurate overall, with the vast majority receiving the top score. The mean accuracy and completeness of ChatGPT were 5.96 (SD = 0.17) and 2.93 (SD = 0.25) and in Bing were 5.31 (SD = 0.67), and 2.13 (SD = 0.53) Respectively. Due to the expansion of artificial intelligence applications, patients can use new tools such as ChatGPT and Bing to search for information and at the same time can trust the information obtained. we found that the answers obtained from ChatGPT are reliable and valuable for patients, while Bing is also considered a powerful tool, it has more limitations than ChatGPT, and the answers should be interpreted with caution.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

The First Months of Life of ChatGPT and Its Impact in Healthcare: A Bibliometric Analysis of the Current Literature

We aimed to evaluate current trends and future directions in the field of AI research since ChatGPT was launched. We performed a bibliometric analysis of the literature published during the first 7 months of the life of ChatGPT since its introduction, updated to July 1st, 2023. Seven hundred and twenty-four (724) articles were retrieved. This analysis highlights a significant increase in publications exploring ChatGPT use across various medical disciplines, indicating its expanding relevance in healthcare. A decline proportion of studies focusing on ethical considerations was observed. Simultaneously, there was a steady increase in studies focused on the exploration of possible applications of ChatGPT. As ChatGPT applications continue to expand, ongoing vigilance and collaborative efforts to optimize ChatGPT performance are essential in harnessing the benefits while mitigating the risks of AI use in healthcare.

Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients

INTRODUCTION

Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system.

MATERIALS AND METHODS

Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated.

RESULTS

ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively.

CONCLUSIONS

The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.

The impact of serum BNP on retinal perfusion assessed by an AI-based denoising optical coherence tomography angiography in CHD patients

Background

To investigate the correlation between retinal vessel density (VD) parameters with serum B-type natriuretic peptide (BNP) in patients with coronary heart disease (CHD) using novel optical coherence tomography angiography (OCTA) denoising images based on artificial intelligence (AI).

Methods

OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device in 176 patients with CHD. Baseline information and blood test results were recorded.

Results

Retinal VD parameters of the macular and optic nerves on OCTA were significantly decreased in patients with CHD after denoising. Retinal VD of the superficial capillary plexus (SCP), deep capillary plexus (DCP) and radial peripapillary capillary (RPC) was strongly correlated with serum BNP levels in patients with CHD. Significant differences were noted in retinal thickness and retinal VD (SCP, DCP and RPC) between the increased BNP and normal BNP groups in patients with CHD.

Conclusion

Deep learning denoising can remove background noise and smooth rough vessel surfaces. SCP,DCP and RPC may be potential clinical markers of cardiac function in patients with CHD. Denoising shows great potential for improving the sensitivity of OCTA images as a biomarker for CHD progression.

Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model

Background

Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration.

Methods

We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio.

Results

This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. Conclusion: The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset"

The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scans. The proposed algorithm attains impressive performance on the test dataset with a mean Area Under the Receiver-Operating Characteristic curve of 0.98 and a mean accuracy of 88%. In this letter, I raise concerns that the reported results can be explained by inadvertent data leakage between training and test datasets. More precisely, I conjecture that the random split of the full dataset in training and test sets did not occur on a patient level, but rather on the level of 2D MRI slices. This allows the neural network to "memorize" a patient's anatomy and their treatment outcome, as opposed to discovering useful features for treatment response prediction. To provide evidence for these claims, I present results of similar experiments I conducted on a public breast MRI dataset, where I demonstrate that the suspected data leakage mechanism closely reproduces the results reported on in the cited work.

Ten computational challenges in human virome studies

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various types of virome studies. These challenges arise due to the vast diversity of viromes, the absence of a universal marker gene in viral genomes, the low abundance of virus populations, the remote or minimal homology of viral proteins to known proteins, and the highly dynamic and heterogeneous nature of viromes. For each computational challenge, we discuss the underlying reasons, current research progress, and potential solutions. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study

This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.

Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation

OBJECTIVE

Automated identification of eligible patients is a bottleneck of clinical research. We propose Criteria2Query (C2Q) 3.0, a system that leverages GPT-4 for the semi-automatic transformation of clinical trial eligibility criteria text into executable clinical database queries.

MATERIALS AND METHODS

C2Q 3.0 integrated three GPT-4 prompts for concept extraction, SQL query generation, and reasoning. Each prompt was designed and evaluated separately. The concept extraction prompt was benchmarked against manual annotations from 20 clinical trials by two evaluators, who later also measured SQL generation accuracy and identified errors in GPT-generated SQL queries from 5 clinical trials. The reasoning prompt was assessed by three evaluators on four metrics: readability, correctness, coherence, and usefulness, using corrected SQL queries and an open-ended feedback questionnaire.

RESULTS

Out of 518 concepts from 20 clinical trials, GPT-4 achieved an F1-score of 0.891 in concept extraction. For SQL generation, 29 errors spanning seven categories were detected, with logic errors being the most common (n = 10; 34.48 %). Reasoning evaluations yielded a high coherence rating, with the mean score being 4.70 but relatively lower readability, with a mean of 3.95. Mean scores of correctness and usefulness were identified as 3.97 and 4.37, respectively.

CONCLUSION

GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. Continued research is warranted to ensure the reliability of large language models.

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography

BACKGROUND

Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is often used for RLS diagnosis. However, the identification of saline microbubbles in the left heart can be challenging for novice residents, potentially leading to a delay in diagnosis and treatment. In this study, we proposed an artificial intelligence (AI)-based algorithm designed to automatically detect microbubbles in scTTE images and evaluate right-to-left shunt grades. This tool aims to support residency training and decrease the workload of cardiologists.

METHODS

A dataset of 23,665 scTTE images obtained from 174 individuals was included in this study. This dataset was partitioned into a training set (n = 20,475) and an internal validation set (n = 3,190) on a patient-level basis. An additional cohort of 33 patients diagnosed with cryptogenic ischemic stroke was enrolled as an external validation set. The proposed algorithm for right-to-left shunt degree classification employed the EfficientNet-b4 model, and the model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, and compared to the performance of residents.

RESULTS

Our AI model demonstrated robust performance with an accuracy of 0.926, sensitivity of 0.827, and specificity of 0.951 on the internal testing dataset. In the external validation set, our AI model exhibited diagnostic accuracy, sensitivity, and specificity of 0.864, 0.818, and 0.909, respectively. In comparison, residents achieved values of 0.727, 0.636, and 0.818, respectively.

CONCLUSION

Our AI model provides a swift, precise, and easily deployable methodology for grading the degree of right-to-left shunt in scTTE, carrying substantial implications for routine clinical practice. Residents can benefit from our artificial intelligence-based algorithm, enhancing both the accuracy and efficiency of RLS diagnosis.

Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure

BACKGROUND

Heart failure (HF) is a prevalent condition associated with significant morbidity. Patients may have questions that they feel embarrassed to ask or will face delays awaiting responses from their healthcare providers which may impact their health behavior. We aimed to investigate the potential of large language model (LLM) based artificial intelligence (AI) chat platforms in complementing the delivery of patient-centered care.

METHODS

Using online patient forums and physician experience, we created 30 questions related to diagnosis, management and prognosis of HF. The questions were posed to two LLM-based AI chat platforms (OpenAI's ChatGPT-3.5 and Google's Bard). Each set of answers was evaluated by two HF experts, independently and blinded to each other, for accuracy (adequacy of content) and consistency of content.

RESULTS

ChatGPT provided mostly appropriate answers (27/30, 90%) and showed a high degree of consistency (93%). Bard provided a similar content in its answers and thus was evaluated only for adequacy (23/30, 77%). The two HF experts' grades were concordant in 83% and 67% of the questions for ChatGPT and Bard, respectively.

CONCLUSION

LLM-based AI chat platforms demonstrate potential in improving HF education and empowering patients, however, these platforms currently suffer from issues related to factual errors and difficulty with more contemporary recommendations. This inaccurate information may pose serious and life-threatening implications for patients that should be considered and addressed in future research.

The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model

Artificial intelligence (AI) and other advanced technologies are increasingly recognized as essential catalysts for enhancing productivity due to their capability to transform nearly all operations within and outside firms. However, the empirical research on how AI assimilation may promote firm-level outcomes such as absorptive capacity (AC), customer agility (CA), and firm performance (FP) is still in its infancy. Drawing from the dynamic capability view and using 417 valid responses collected through cross-sectional methods from small and medium-sized enterprises (SMEs) in Lebanon, this study examines the effect of AI assimilation on firm performance. The mediating roles of AC and CA were investigated. The moderating role of organizational agility (OA) was also explored. The findings support the hypothesized assumptions that continual advancement of technology evolves the industrial organizations' performance with CA and AC as parallel mediators, partially mediating the link between AI assimilation and FP and OA as a moderator, moderating the positive relationship between AI and CA and between AI and FP. The findings provide crucial insights for practitioners and advance the dynamic capability view framework. They provide compelling evidence that enriches the understanding of AI assimilation, demonstrating its positive impact on critical organizational outcomes and yielding performance benefits for SMEs.

Evaluating the accuracy and reproducibility of ChatGPT-4 in answering patient questions related to small intestinal bacterial overgrowth

BACKGROUND

Small intestinal bacterial overgrowth (SIBO) poses diagnostic and treatment challenges due to its complex management and evolving guidelines. Patients often seek online information related to their health, prompting interest in large language models, like GPT-4, as potential sources of patient education.

AIM

To investigate ChatGPT-4's accuracy and reproducibility in responding to patient questions related to SIBO.

METHODS

A total of 27 patient questions related to SIBO were curated from professional societies, Facebook groups, and Reddit threads. Each question was entered into GPT-4 twice on separate days to examine reproducibility of accuracy on separate occasions. GPT-4 generated responses were independently evaluated for accuracy and reproducibility by two motility fellowship-trained gastroenterologists. A third senior fellowship-trained gastroenterologist resolved disagreements. Accuracy of responses were graded using the scale: (1) Comprehensive; (2) Correct but inadequate; (3) Some correct and some incorrect; or (4) Completely incorrect. Two responses were generated for every question to evaluate reproducibility in accuracy.

RESULTS

In evaluating GPT-4's effectiveness at answering SIBO-related questions, it provided responses with correct information to 18/27 (66.7%) of questions, with 16/27 (59.3%) of responses graded as comprehensive and 2/27 (7.4%) responses graded as correct but inadequate. The model provided responses with incorrect information to 9/27 (33.3%) of questions, with 4/27 (14.8%) of responses graded as completely incorrect and 5/27 (18.5%) of responses graded as mixed correct and incorrect data. Accuracy varied by question category, with questions related to “basic knowledge” achieving the highest proportion of comprehensive responses (90%) and no incorrect responses. On the other hand, the “treatment” related questions yielded the lowest proportion of comprehensive responses (33.3%) and highest percent of completely incorrect responses (33.3%). A total of 77.8% of questions yielded reproducible responses.

CONCLUSION

Though GPT-4 shows promise as a supplementary tool for SIBO-related patient education, the model requires further refinement and validation in subsequent iterations prior to its integration into patient care.

TriKSV-LG: a robust approach to disease prediction in healthcare systems using AI and Levy Gazelle optimization

A seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems. The IoT sensors collect information about patients' health conditions, and the AI employs the information in disease prediction. TriKSV utilizes multiple kernel functions, including linear, polynomial, and radial basis functions, to classify features more effectively. By learning from different representations of the data, TriKSV better handles variations and complexities within the dataset, leading to more robust disease prediction models. The Levy Flight strategy with Gazelle optimization algorithm tunes the hyperparameters and balances the exploration and exploitation for optimal hyperparameter configurations in predicting chronic kidney disease (CKD) and heart disease (HD). Furthermore, TriKSV's incorporation of multiple kernel functions, combined with the Gazelle optimization strategy, helps mitigate overfitting by providing a more comprehensive search space for optimal hyperparameter selection. The proposed TriKSV-LG method is applied to two different datasets, namely the CKD dataset and the HD dataset, and evaluated using performance measures such as AUC-ROC, specificity, F1-score, recall, precision, and accuracy. The results demonstrate that the proposed TriKSV-LG method achieved an accuracy of 98.56% in predicting kidney disease using the CKD dataset and 98.11% accuracy in predicting HD using the HD dataset.

Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning

The advancement of artificial intelligence (AI) and the ubiquity of social media have become transformative agents in contemporary educational ecosystems. The spotlight of this inquiry focuses on the nexus between AI and social media usage in relation to academic performance and mental well-being, and the role of smart learning in facilitating these relationships. Using partial least squares-structural equation modeling (PLS-SEM) on a sample of 401 Chinese university students. The study results reveal that both AI and social media have a positive impact on academic performance and mental well-being among university students. Furthermore, smart learning serves as a positive mediating variable, amplifying the beneficial effects of AI and social media on both academic performance and mental well-being. These revelations contribute to the discourse on technology-enhanced education, showing that embracing AI and social media can have a positive impact on student performance and well-being.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study

OBJECTIVE

To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL).

METHODS

The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance.

RESULTS

In internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740-0.811) and 0.720 (95 % CI 0.684-0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798-0.940) and 0.851 (95 % CI 0.756-0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743-0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682-0.755) to 0.808 (95 % CI 0.775-0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608-0.686) to 0.807 (95 % CI 0.773-0.837) for junior otolaryngologists.

CONCLUSIONS

The MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.

Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data

Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.

Artificial intelligence to predict soil temperatures by development of novel model

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy

BACKGROUND

Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed.

METHODS

A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed.

RESULTS

Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function.

CONCLUSION

Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.

Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

BACKGROUND AND OBJECTIVE

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone.

METHODS

A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test.

KEY FINDINGS AND LIMITATIONS

Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

PATIENT SUMMARY

The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

Performance of ChatGPT in Answering Clinical Questions on the Practical Guideline of Blepharoptosis

BACKGROUND

ChatGPT is a free artificial intelligence (AI) language model developed and released by OpenAI in late 2022. This study aimed to evaluate the performance of ChatGPT to accurately answer clinical questions (CQs) on the Guideline for the Management of Blepharoptosis published by the American Society of Plastic Surgeons (ASPS) in 2022.

METHODS

CQs in the guideline were used as question sources in both English and Japanese. For each question, ChatGPT provided answers for CQs, evidence quality, recommendation strength, reference match, and answered word counts. We compared the performance of ChatGPT in each component between English and Japanese queries.

RESULTS

A total of 11 questions were included in the final analysis, and ChatGPT answered 61.3% of these correctly. ChatGPT demonstrated a higher accuracy rate in English answers for CQs compared to Japanese answers for CQs (76.4% versus 46.4%; p = 0.004) and word counts (123 words versus 35.9 words; p = 0.004). No statistical differences were noted for evidence quality, recommendation strength, and reference match. A total of 697 references were proposed, but only 216 of them (31.0%) existed.

CONCLUSIONS

ChatGPT demonstrates potential as an adjunctive tool in the management of blepharoptosis. However, it is crucial to recognize that the existing AI model has distinct limitations, and its primary role should be to complement the expertise of medical professionals.

LEVEL OF EVIDENCE V

Observational study under respected authorities. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Use of Virtual Reality and 3D Models in Contemporary Practice of Cardiology

PURPOSE OF REVIEW

To provide an overview of the impact of virtual and augmented reality in contemporary cardiovascular medical practice.

RECENT FINDINGS

The utilization of virtual and augmented reality has emerged as an innovative technique in various cardiovascular subspecialties, including interventional adult, pediatric, and adult congenital as well as structural heart disease and heart failure. In particular, electrophysiology has proven valuable for both diagnostic and therapeutic procedures. The incorporation of 3D reconstruction modeling has significantly enhanced our understanding of patient anatomy and morphology, thereby improving diagnostic accuracy and patient outcomes. The interactive modeling of cardiac structure and function within the virtual realm plays a pivotal role in comprehending complex congenital, structural, and coronary pathology. This, in turn, contributes to safer interventions and surgical procedures. Noteworthy applications include septal defect device closure, transcatheter valvular interventions, and left atrial occlusion device implantation. The implementation of virtual reality has been shown to yield cost savings in healthcare, reduce procedure time, minimize radiation exposure, lower intravenous contrast usage, and decrease the extent of anesthesia required. These benefits collectively result in a more efficient and effective approach to patient care.

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm

OBJECTIVES

To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans.

MATERIALS AND METHODS

Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT".

RESULTS

The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification.

CONCLUSION

An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability.

CLINICAL RELEVANCE STATEMENT

Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging.

KEY POINTS

Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

Demand-side load forecasting in smart grids using machine learning techniques

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task

Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.

Groundwater salinization risk assessment using combined artificial intelligence models

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.